A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications
Wang, Kunfeng1; Liu, Yuqiang1,2; Gou, Chao1,3; Wang, Fei-Yue1,4; Wang, Kunfeng(王坤峰)
Source PublicationIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
2016-06-01
Volume65Issue:6Pages:4144-4158
SubtypeArticle
AbstractThis paper proposes an effective multi-view learning approach to foreground detection for traffic surveillance applications. This approach involves three main steps. First, a reference background image is generated via temporal median filtering, and multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation, each of which represents a unique view) are extracted from the video sequence. Then, a multi-view learning strategy is devised to online estimate the conditional probability densities for both the foreground and the background. The probability densities of three features are approximately conditionally independent and are estimated with kernel density estimation. Pixel soft labeling is conducted by using Bayes rule, and the pixelwise foreground posteriors are computed. Finally, a Markov random field is constructed to incorporate the spatiotemporal context into the foreground/background decision model. The belief propagation algorithm is used to label each pixel of the current frame. Experimental results verify that the proposed approach is effective to detect foreground objects from challenging traffic environments and outperforms some state-of-the-art methods.
KeywordConditional Independence Foreground Detection Heterogeneous Features Markov Random Field (Mrf) Multi-view Learning
WOS HeadingsScience & Technology ; Technology
Subject AreaCivil Engineering
DOI10.1109/TVT.2015.2509465
WOS KeywordGAUSSIAN MIXTURE MODEL ; REAL-TIME TRACKING ; BACKGROUND SUBTRACTION ; OBJECT DETECTION ; VISUAL SURVEILLANCE ; CAST SHADOWS ; SEGMENTATION ; SUPPRESSION ; EDGE
URL查看原文
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61304200) ; MIIT Project of Internet of Things Development Fund(1F15E02)
WOS Research AreaEngineering ; Telecommunications ; Transportation
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS IDWOS:000380068500026
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10859
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorWang, Kunfeng(王坤峰)
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.China Acad Railway Sci, Beijing 100081, Peoples R China
3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
4.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst, Changsha 410073, Hunan, Peoples R China
Recommended Citation
GB/T 7714
Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,et al. A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2016,65(6):4144-4158.
APA Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,Wang, Fei-Yue,&Wang, Kunfeng.(2016).A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,65(6),4144-4158.
MLA Wang, Kunfeng,et al."A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 65.6(2016):4144-4158.
Files in This Item: Download All
File Name/Size DocType Version Access License
2016A Multi-View Lea(1153KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Kunfeng]'s Articles
[Liu, Yuqiang]'s Articles
[Gou, Chao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Kunfeng]'s Articles
[Liu, Yuqiang]'s Articles
[Gou, Chao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Kunfeng]'s Articles
[Liu, Yuqiang]'s Articles
[Gou, Chao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2016A Multi-View Learning Approach to Foreground Detection for Traffic Surveillance Applications.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.